Salient object detection (SOD) plays a critical role in vision-driven measurement systems (VMS), facilitating the detection and segmentation of key visual elements in an image. However, adverse imaging conditions such as haze during the day, low light, and haze at night severely degrade image quality, and complicating the SOD process. To address these challenges, we propose a multi-task-oriented nighttime haze imaging enhancer (MToIE), which integrates three tasks: daytime dehazing, low-light enhancement, and nighttime dehazing. The MToIE incorporates two key innovative components: First, the network employs a task-oriented node learning mechanism to handle three specific degradation types: day-time haze, low light, and night-time haze conditions, with an embedded self-attention module enhancing its performance in nighttime imaging. In addition, multi-receptive field enhancement module that efficiently extracts multi-scale features through three parallel depthwise separable convolution branches with different dilation rates, capturing comprehensive spatial information with minimal computational overhead. To ensure optimal image reconstruction quality and visual characteristics, we suggest a hybrid loss function. Extensive experiments on different types of weather/imaging conditions illustrate that MToIE surpasses existing methods, significantly enhancing the accuracy and reliability of vision systems across diverse imaging scenarios. The code is available at https://github.com/Ai-Chen-Lab/MKoIE.

翻译：显著目标检测在视觉驱动测量系统中扮演着关键角色，有助于检测和分割图像中的关键视觉元素。然而，白天雾霾、低光照和夜间雾霾等不利成像条件会严重降低图像质量，并使显著目标检测过程复杂化。为应对这些挑战，我们提出了一种面向多任务的夜间雾霾成像增强器，该模型整合了三个任务：白天去雾、低光照增强和夜间去雾。MToIE包含两个关键创新组件：首先，网络采用任务导向的节点学习机制来处理三种特定退化类型——白天雾霾、低光照和夜间雾霾条件，其中嵌入的自注意力模块提升了其在夜间成像中的性能。此外，多感受野增强模块通过三个具有不同膨胀率的并行深度可分离卷积分支高效提取多尺度特征，以最小的计算开销捕获全面的空间信息。为确保最优的图像重建质量和视觉特性，我们提出了一种混合损失函数。在不同类型天气/成像条件下的大量实验表明，MToIE超越了现有方法，显著提升了视觉系统在多样化成像场景中的准确性和可靠性。代码发布于https://github.com/Ai-Chen-Lab/MKoIE。